Study of Persistent Haze Pollution in Winter over Jinan (China) Based on Ground-Based and Satellite Observations

Li, Hui; Shi, Rui; Jin, Shikuan; Wang, Weiyan; Fan, Ruonan; Yi-qun, Zhang; Liu, Boming; Zhao, Peitao; Gong, Wei; Zhao, Yuefeng

doi:10.3390/rs13234862

Cited by 9 publications

(3 citation statements)

References 54 publications

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“…These results indicate that the performances of the four classifiers are similar under this state. Li et al [42] also pointed out that the retrieved ABLH results of various methods were similar when the aerosols were uniformly distributed below the boundary layer. The clustering results for the different classifiers on 23 October 2015 are shown in Figure 6.…”

Section: Case Studymentioning

confidence: 92%

A Cluster Analysis Approach for Nocturnal Atmospheric Boundary Layer Height Estimation from Multi-Wavelength Lidar

Zhu

Zhou

et al. 2023

Atmosphere

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The atmospheric boundary layer provides useful information about the accumulation and diffusion of pollutants. As a fast method, remote sensing techniques are used to retrieve the atmospheric boundary layer height (ABLH). Atmospheric detection lidar has been widely applied for retrieving the ABLH by providing information on the vertical distribution of aerosols. However, these previous algorithms that rely on gradient change are susceptible to residual layers. Contrary to the use of gradient change to retrieve ABLH, in this paper, we propose using a cluster analysis approach through multifunction lidar remote sensing techniques due to its increasing availability. The clustering algorithm for multi-wavelength lidar data can be divided into two parts: characteristic signal selection and selection of the classifier. First, since the separability of each type of signal is different, careful selection of the input characteristic signal is important. We propose using Fourier transform for all the observed signals; the most suitable characteristic signal can be determined based on the dispersion degree of the signal in the frequency domain. Then, the performances of four common classifiers (K-means method, Gaussian mixture model, hierarchical cluster method (HCM), and density-based spatial clustering of applications with noise) are evaluated by comparing with the radiosonde measurements from June 2015 to June 2016. The results show that the performance of the HCM classifier is the best under all states (R2 = 0.84 and RMSE = 0.18 km). The findings obtained here offer insight into ABLH remote sensing technology.

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Section: Case Studymentioning

confidence: 92%

A Cluster Analysis Approach for Nocturnal Atmospheric Boundary Layer Height Estimation from Multi-Wavelength Lidar

Zhu

Zhou

et al. 2023

Atmosphere

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“…This task involves categorizing GNSS trajectory points of agricultural machinery into 'field' and 'road' categories, which plays a crucial role in crop improvement [6][7][8][9]. Accurate classification results are essential for estimating the areas of agricultural fields precisely [10][11][12][13][14]. This estimation contributes to efficiently budgeting the input amount of agricultural production materials (e.g., seeds, fertilizers, and pesticides), which ultimately increases crop yield and improves agricultural productivity [15].…”

Section: Introductionmentioning

confidence: 99%

ConvTEBiLSTM: A Neural Network Fusing Local and Global Trajectory Features for Field-Road Mode Classification

Bian,

Bai,

Cheng

et al. 2024

IJGI

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Field-road mode classification (FRMC) that identifies “in-field” and “on-road” categories for Global Navigation Satellite System (GNSS) trajectory points of agricultural machinery containing geographic information is essential for effective crop improvement. Most previous studies utilize local trajectory features (i.e., the relationships between a point and its neighboring points), but they ignore global trajectory features (i.e., the relationships between the point and all points of the trajectory), leading to difficulty in improving the overall classification performance. The global trajectory features are useful for FRMC because they contain rich trajectory information (e.g., mode switching and motion tendency). Therefore, a ConvTEBiLSTM network-based method is proposed to improve the overall performance. Firstly, nine statistical features (e.g., speed and direction) are extracted from the original data and fed into the ConvTEBiLSTM network. Then, the ConvTEBiLSTM network combining the Bidirectional Long Short-Term Memory network, 1D Convolution network, and Transformer-Encoder network is used to extract and fuse local and global trajectory features. Finally, a linear classifier is applied to identify the “field” and “road” categories of GNSS points based on the fused features. Experimental results show that compared with the baselines, our method achieves the best accuracy and F1-score of 97.38% and 92.74% on our Harvester dataset, respectively.

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“…The TI inhibits the flow of heat from the surface into the free air and slows down the diffusion of pollutants [5][6][7][8][9]. TIs, both surface-based inversion (SBI) and elevated inversion (EI), effectively ensnare aerosols in the boundary layer and keep them from dispersing [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

The Influence of Temperature Inversion on the Vertical Distribution of Aerosols

Liu

et al. 2022

Remote Sensing

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Temperature inversion plays an important role in the accumulation and diffusion of aerosols. In this study, the relationship between temperature inversion and the vertical distribution of aerosols is investigated based on Raman lidar observations taken from January 2010 to September 2015 at the Atmospheric Radiation Measurement site in the Southern Great Plains, USA. First, the diurnal and seasonal variations of the surface-based inversion (SBI) and elevated temperature inversion (EI) are investigated. The results indicate that the occurrence frequency of SBI and EI have different seasonal trends. SBI has the highest frequency in summer, while EI has the highest frequency in winter. The diurnal variation of SBI is obvious, with a higher frequency in nighttime and a lower frequency in daytime. The inversion intensity (ΔT) and inversion depth (ΔZ) of SBI and EI have consistent diurnal and seasonal trends. The effects of SBI and EI on the vertical distribution of aerosols are then analyzed. The mean aerosol optical depth (AOD) below the SBI height shows a clear seasonal variation, which is consistent with the seasonal trends of ΔT and ΔZ. This phenomenon also occurs on the AOD below EI top height. The sensitivity analysis shows that the mean AOD below SBI height or EI top height increases with an increase of the ΔT and ΔZ of SBI (EI). It indicates that ΔT and ΔZ are the key factors affecting the vertical distribution of aerosols. In addition, the variation of AOD below and above EI top height is opposite to that of AOD below and above EI bottom height under different ΔT and ΔZ conditions. The correlation coefficients between ΔT (ΔZ) of EI with AOD in EI were 0.62 (0.65). These results indicate that the space between EI bottom height and EI top height can store aerosols. The larger the ΔZ of EI, the more aerosols are stored. These findings contribute to our understanding of the effect of temperature inversion on the vertical distribution of aerosols.

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Study of Persistent Haze Pollution in Winter over Jinan (China) Based on Ground-Based and Satellite Observations

Cited by 9 publications

References 54 publications

A Cluster Analysis Approach for Nocturnal Atmospheric Boundary Layer Height Estimation from Multi-Wavelength Lidar

A Cluster Analysis Approach for Nocturnal Atmospheric Boundary Layer Height Estimation from Multi-Wavelength Lidar

ConvTEBiLSTM: A Neural Network Fusing Local and Global Trajectory Features for Field-Road Mode Classification

The Influence of Temperature Inversion on the Vertical Distribution of Aerosols

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